PSO–LSTM for short term forecast of heterogeneous time series electricity price signals

Electricity price forecasting plays an important role in the power system network, in order to promote the decision-making process for power generation and consumption. Long term forecasting is not viable as there is an uncertainty in the forecast due to increasing the integration of renewable sources with the existing grids. Since the behavior of the electricity price time sequence signal is highly non-linear and seasonal, deep neural network is the best model for learning the non-linear behavior within the data and for the purpose of forecasting. Hence this paper proposes an enhanced particle swarm optimization based long short-term memory (LSTM) neural network model, which is used to forecast the closing price of the Indian Energy Exchange. Particle swarm optimization technique is used to optimize the LSTM network input weights, which in turn minimize the forecast error with reduced architecture. This paper discusses the statistical analysis for input data selection and investigates the performance analysis for the optimal selection of layers with hidden units’ combination. Finally, the analysis deploys the best-suited configuration for forecasting the market clearing price with the least mean absolute percentage error.